"Medicine is a social science, and politics is nothing else but medicine on a large scale"—Rudolf Virchow

May 21, 2014

Random Analytics: Granger on MERS by key occupation

Via his blog Random Analytics, Shane Granger has posted MERS by Key Occupation (to +300). Excerpt from a remarkable analytic that is anything but random. Click through for the full post, and while you're at it, you might as well follow him:

This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Key Notes:

The most represented by the data are Non-Participatory. It should be noted that I don’t believe that this data is a true representation and there is a lot of ‘hidden’ Pilgrims, Employees (unspecified) and Tourists in this data;

• Healthcare Prac/Tech Ops are overrepresented in the known data, due to significant outbreaks in hospitals across Saudi Arabia. This is now (in my mind) a proven data point as the new Health Minister, Adel M. Fakieh, has effectively suppressed any new data on HCW infections since he took over the job but they still represent 16.7% of my data;

• Healthcare Support gets a mention (finally). At less than 1% (confirmed) I think the actual number is much higher given the good data comes out of the UAE. The Saudis (again) are not discussing HCW but having worked in an Operating Theatre myself as a non-HCW (I was an Anaesthetic Secretary) it is easy to see how these workers become infected;

• Paediatric(s) only make up 2.8% of the data inputs. Seems low, especially compared against H7N9 but I’m no virologist, just an amateur flublogist.

Last chart.

The last chart looks at those overall main job families that are most impacted by MERS-CoV (specifically Farmers, Travellers, Paediatrics, Retired, HCW’s, Other and Unknown).

Key Notes:

• Farmer (2.0%): It is thought that MERS-CoV is initially spread by the handling of camels. Not represented by the known data so probably underrepresented or the wrong narrative.

• Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Again, not strongly represented by the data. I suspect it is suppressed data.

• Paediatrics (2.8%): Any children reported between 0 – 14 years of age are here. Data doesn’t seem to show strong family clusters but I wonder given the P2P data we do know (amongst HCW’s) seem to show strong secondary infections amongst close working colleagues.

• HCW (16.9%): Health Care Workers of any description but doesn’t include Health Support workers. Often a good indicator of secondary infections potency. Subject Matter Expert(s) on HCW infections are m’coll’s Ian Mackay and Maia Majumder.

• Other (3.1%): All other occupations that have been publically released.

• Unknown (53.2%): Unknown occupations. It should be noted that I have ‘guessed’ 136 occupations as ‘Retired’.

Final Thoughts

I now call on Professor Crawford Kilian to add Adel M. Fakieh to the Supari Prize list along with the previous Saudi Health Minister. As many have noted although we saw an initial uptick in publically sourced data from the Saudi MoH the data has now precluded data around occupation (HCW specifically) and expatriate status. This is now pure suppression of data and will no doubt lead to more cases ‘exporting’ from the Kingdom to other countries, including the United States which has now experienced two exported cases and one secondary infection.

I concur with the charge. Adel Faqih (spellings vary) has done a much better PR job of seeming to be transparent, but in some ways we're more ignorant now than we were under Al-Rabiah's reign.

Comments

Random Analytics: Granger on MERS by key occupation

Via his blog Random Analytics, Shane Granger has posted MERS by Key Occupation (to +300). Excerpt from a remarkable analytic that is anything but random. Click through for the full post, and while you're at it, you might as well follow him:

This infographic looks at those infected with MERS-CoV by Job Family. In short I think this is a key infographic for MERS as it gives you some confidence in the key narratives (i.e. that Health Care Workers are over represented in the data as an example).

Key Notes:

The most represented by the data are Non-Participatory. It should be noted that I don’t believe that this data is a true representation and there is a lot of ‘hidden’ Pilgrims, Employees (unspecified) and Tourists in this data;

• Healthcare Prac/Tech Ops are overrepresented in the known data, due to significant outbreaks in hospitals across Saudi Arabia. This is now (in my mind) a proven data point as the new Health Minister, Adel M. Fakieh, has effectively suppressed any new data on HCW infections since he took over the job but they still represent 16.7% of my data;

• Healthcare Support gets a mention (finally). At less than 1% (confirmed) I think the actual number is much higher given the good data comes out of the UAE. The Saudis (again) are not discussing HCW but having worked in an Operating Theatre myself as a non-HCW (I was an Anaesthetic Secretary) it is easy to see how these workers become infected;

• Paediatric(s) only make up 2.8% of the data inputs. Seems low, especially compared against H7N9 but I’m no virologist, just an amateur flublogist.

Last chart.

The last chart looks at those overall main job families that are most impacted by MERS-CoV (specifically Farmers, Travellers, Paediatrics, Retired, HCW’s, Other and Unknown).

Key Notes:

• Farmer (2.0%): It is thought that MERS-CoV is initially spread by the handling of camels. Not represented by the known data so probably underrepresented or the wrong narrative.

• Traveller (1.2%): One of the great concerns was of pilgrims and tourists spreading the disease far and wide. Again, not strongly represented by the data. I suspect it is suppressed data.

• Paediatrics (2.8%): Any children reported between 0 – 14 years of age are here. Data doesn’t seem to show strong family clusters but I wonder given the P2P data we do know (amongst HCW’s) seem to show strong secondary infections amongst close working colleagues.

• HCW (16.9%): Health Care Workers of any description but doesn’t include Health Support workers. Often a good indicator of secondary infections potency. Subject Matter Expert(s) on HCW infections are m’coll’s Ian Mackay and Maia Majumder.

• Other (3.1%): All other occupations that have been publically released.

• Unknown (53.2%): Unknown occupations. It should be noted that I have ‘guessed’ 136 occupations as ‘Retired’.

Final Thoughts

I now call on Professor Crawford Kilian to add Adel M. Fakieh to the Supari Prize list along with the previous Saudi Health Minister. As many have noted although we saw an initial uptick in publically sourced data from the Saudi MoH the data has now precluded data around occupation (HCW specifically) and expatriate status. This is now pure suppression of data and will no doubt lead to more cases ‘exporting’ from the Kingdom to other countries, including the United States which has now experienced two exported cases and one secondary infection.

I concur with the charge. Adel Faqih (spellings vary) has done a much better PR job of seeming to be transparent, but in some ways we're more ignorant now than we were under Al-Rabiah's reign.